Probabilistic Presurgical Language fMRI Atlas of Patients with Brain Tumors
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND PURPOSE: Patients with brain tumors have high intersubject variation in putative language regions, which may limit the utility of straightforward application of healthy-subject brain atlases in clinical scenarios. The purpose of this study was to develop a probabilistic functional brain atlas that consolidates language functional activations of sentence completion and silent word generation language paradigms using a large sample of patients with brain tumors. MATERIALS AND METHODS: The atlas was developed using retrospectively collected fMRI data from patients with brain tumors who underwent their first standard-of-care presurgical language fMRI scan at our institution between July 18, 2015, and May 13, 2022. 317 patients (861 fMRI scans) were used to develop the language functional atlas. An independent presurgical language fMRI dataset of 39 patients with brain tumors from a previous study was used to evaluate our atlas. Family-wise error corrected binary functional activation maps from sentence completion, letter fluency, and category fluency presurgical fMRI were used to create probability overlap maps and pooled probabilistic overlap map in Montreal Neurological Institute standard space. Wilcoxon signed-rank test was used to determine significant difference in the maximum Dice coefficient for our atlas compared to a meta-analysis-based template with respect to expert-delineated primary language area activations. RESULTS: Probabilities of activating left anterior primary language area and left posterior primary language area in temporal lobe were 87.9% and 91.5%, respectively, for sentence completion, 88.5% and 74.2%, respectively, for letter fluency, and 83.6% and 67.6%, respectively, for category fluency. Maximum Dice coefficients for templates derived from our language atlas were significantly higher than the meta-analysis-based template in left anterior primary language area (0.351 and 0.326, respectively, P < .05) and left posterior primary language area in temporal lobe (0.274 and 0.244, respectively, P < .005). CONCLUSIONS: Brain tumor patient-and paradigm-specific probabilistic language atlases were developed. These atlases had superior spatial agreement with fMRI activations in individual patients than the meta-analysis-based template. ABBREVIATIONS: SENT = sentence completion, LETT = letter fluency, CAT = category fluency, PLA = primary language area, aPLA = anterior PLA, pPLAT = posterior PLA in the temporal lobe, pPLAP = posterior PLA in the parietal lobe, SMA = supplementary motor area, DLPFC = dorsolateral prefrontal cortex, BTLA = basal temporal language area
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".